Abstract

Haze is the natural phenomenon, which affects an image's air light and visibility. It creates a layer that hides the information in an acquired hazy image and decreases its visibility. Hazy scenarios are mostly seen in the transportation sector and remote sensing. It affects the quality of an image captured. Haze is one of the major hurdles in several computer vision applications. This paper observes and analyses different methods of haze removal via image enhancement techniques. Proposes the weighted average of the image enhancement methods to generate the enhanced hazy input image as the initial step. These enhanced images do train the neural network to estimate transmission map as well as atmospheric light, used for haze removal from images. The proposed method is experimented with 135 hazy images from three standard datasets, alias I-Haze, NH-Haze, and O-Haze  (45 images from each total 135 hazy images). It gives clearer results than a few similar existing haze removal techniques. Also, the experimental results tested with performance metrics Entropy PSNR, and SSIM have demonstrated the effectiveness of the proposed haze removal method having weighted fusion of pre-processing techniques.

Keywords

Dehazing, Haze, Ai-light, Transmission Map, Image Fusion, White Balance, Contrast Enhancement, Gamma Correction, Histogram Equalization, Neural Network,

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References

  1. F. Guo, J. Yang, Z. Liu & J. Tang, Haze removal for single image: A comprehensive review, Neurocomputing, 537(7), (2023) 85-109. https://doi.org/10.1016/j.neucom.2023.03.061
  2. A. Mittal, R. Soundararajan, A.C. Bovik, Making a 'Completely Blind' Image Quality Analyzer, in IEEE Signal Processing Letters, 20 (2013) 209-212. https://doi.org/10.1109/LSP.2012.2227726
  3. E. Reinhard, M. Adhikhmin, B. Gooch, P. Shirley, Color transfer between images. IEEE Computer Graphics and Applications, 21 (2001) 34-41. https://doi.org/10.1109/38.946629
  4. C.O. Ancuti, C. Ancuti, Single Image Dehazing by Multi-Scale Fusion. in IEEE Transactions on Image Processing, 22 (2013) 3271-3282. https://doi.org/10.1109/TIP.2013.2262284
  5. H. Zhang, Patel, V. M. Densely Connected Pyramid Dehazing Network. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City. https://doi.org/10.1109/CVPR.2018.00337
  6. W. Zhou, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. in IEEE Transactions on Image Processing, 13 (2004) 600-612. https://doi.org/10.1109/TIP.2003.819861
  7. K. He, J. Sun, X. Tang, Single Image Haze Removal Using Dark Channel Prior. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2011) 2341-2353. https://doi.org/10.1109/TPAMI.2010.168
  8. T. Yu, K. Song, P. Miao, G. Yang, H. Yang, C. Chen, Nighttime Single Image Dehazing via Pixel-Wise Alpha Blending. IEEE Access 7 (2019) 114619-114630. https://doi.org/10.1109/ACCESS.2019.2936049
  9. S.D. Thepade, P. Mishra, R. Udgirkar, S. Singh, P. Mengwade, Improved Haze Removal Method Using Proportionate Fusion of Color Attenuation Prior and Edge Preserving. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India. https://doi.org/10.1109/ICCUBEA.2018.8697664
  10. W. Ren, Gated Fusion Network for Single Image Dehazing, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, (2018) 3253-3261. https://doi.org/10.1109/CVPR.2018.00343
  11. W. Mei, X. Li, (2019) Single Image Dehazing Using Dark Channel Fusion and Haze Density Weight, 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 579-585. https://doi.org/10.1109/ICEIEC.2019.8784493
  12. J.H. Kim, W.D. Jang, J.Y. Sim, C.S. Kim, Optimized contrast enhancement for real-time image and video dehazing. Journal of Visual Communication and Image Representation, 24 (2013) 410-425. https://doi.org/10.1016/j.jvcir.2013.02.004
  13. A. Mittal, A.K. Moorthy, A.C. Bovik, No-Reference Image Quality Assessment in the Spatial Domain. in IEEE Transactions on Image Processing, 21 (2012) 4695-4708. https://doi.org/10.1109/TIP.2012.2214050
  14. K. Tang, J. Yang, J. Wang, (2014) Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH. 2995-3002. https://doi.org/10.1109/CVPR.2014.383
  15. Y. Jin, B. Lin, W. Yan, W. Ye, Y. Yuan, R.T. Tan, Enhancing visibility in nighttime haze images using guided APSF and gradient adaptive convolution. In Proceedings of the 31st ACM International Conference on Multimedia, (2023) 2446-2457.
  16. Y. Liu, Z. Yan, J. Tan, & Y. Li, Multi-purpose oriented single nighttime image haze removal based on unified variational retinex model. IEEE Transactions on Circuits and Systems for Video Technology, 33 (2022) 1643-1657. https://doi.org/10.1109/TCSVT.2022.3214430
  17. Y.S. Zuo, & Y. Liang, Defogging method of outdoor scene images based on hybrid contrast enhancement. Computer Simulation, 27 (2010) 227-230.
  18. C.O. Ancuti, C. Ancuti, R. Timofte, & C.D. Vleeschouwer, I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images. Advanced Concepts for Intelligent Vision Systems, 11182 (2018) 620–631. https://doi.org/10.1007/978-3-030-01449-0_52
  19. C.O. Ancuti, C. Ancuti, R. Timofte, & C. De Vleeschouwer, (2018) O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images. IEEE Conference on Computer Vision and Pattern Recognition, USA. https://doi.org/10.1109/CVPRW.2018.00119
  20. C.O. Ancuti, C. Ancuti, R. Timofte, NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), USA. https://doi.org/10.1109/CVPRW50498.2020.00230
  21. B. Li, X. Peng, Z. Wang, J. Xu, D. Feng, AOD-Net: All-in-One Dehazing Network. 2017 IEEE International Conference on Computer Vision (ICCV), Italy. https://doi.org/10.1109/ICCV.2017.511
  22. B. Cai, X. Xu, K. Jia, C. Qing, D. Tao, Dehaze, Net: An End-to-End System for Single Image Haze Removal. In IEEE Transactions on Image Processing, 25 (2016) 5187-5198. https://doi.org/10.1109/TIP.2016.2598681
  23. S.D. Thepade, C.M. Nawale, M.V. Suryavanshi, C.D. Taralkar, A.A. Patil, (2021) Single Image Dehazing using a Weighted Fusion of Dark and Bright Channel Prior with Gamma Correction. 2021 second International Conference for Emerging Technology (INCET), IEEE, India. https://doi.org/10.1109/INCET51464.2021.9456422
  24. S.D. Thepade, A.A. Patil, C.M. Nawale, C.D. Taralkar, M.V. Suryavanshi, (2021) Appraise of Deep Learning and Image Processing based Single Image Dehazing Algorithms. 2nd International Conference for Emerging Technology (INCET), IEEE, India. https://doi.org/10.1109/INCET51464.2021.9456139
  25. D. Abin, S.D. Thepade, S. Dhore, (2021) An Empirical Study of Dehazing Techniques for Chest X-Ray in Early Detection of Pneumonia. 2nd International Conference for Emerging Technology (INCET), IEEE, India. https://doi.org/10.1109/INCET51464.2021.9456201
  26. R.S. Gound, S.D. Thepade, (2021) Removing Haze Influence from Remote Sensing Images Captured with Airborne Visible/ Infrared imaging Spectrometer by Cascaded Fusion of DCP, GF, LCC with AHE. International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), IEEE, India. https://doi.org/10.1109/ICCCIS51004.2021.9397060